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What is Scikit-Learn?

Scikit-Learn (also known as sklearn for short) is a Python library with which machine learning applications can be easily implemented. The library is based on common data structures in Python, such as Numpy, and is therefore very compatible with other modules. The source code of this library can be found on GitHub.

What is Scikit-Learn?

The Scikit-Learn software library enables the use of AI models in the programming language and saves the user a lot of programming effort by integrating common models, such as decision trees or K-Mean clustering, via a few lines of code.

Among the best-known prerequisites for using sklearn are Numpy and SciPy, on which the library is largely based. There are also dependencies on joblib and threadpoolctl. The project was created in 2007 and has since been available on GitHub under the “3-Clause BSD” license.

Which Applications can be implemented with the Library?

Scikit-Learn can be used to implement a wide variety of AI models, from both supervised and unsupervised learning. In general, the models can be divided into the following groups:

In the artificial intelligence environment, the library has only lost a bit of popularity because neural networks have become more and more interesting. These can only be built in a very rudimentary way using Scikit-Learn, which is why many users are switching to Tensorflow, or this library is also becoming more relevant. In addition, neural networks have far surpassed the performance of common AI models.

What are the Advantages of Scikit-Learn?

Benefits of the library include:

  • simplified application of machine learning tools, data analytics, and data visualization
  • Commercial use without licensing fees
  • a high degree of flexibility in fine-tuning models
  • based on common and powerful data structures from Numpy
  • Usable in different contexts

In addition to all the advantages, however, it should be noted with such libraries that the use of machine learning models requires solid prior knowledge and can also simply lead to incorrect statements if used carelessly.

Sklearn makes the use of these models, particularly easy and thus accessible to many users. However, it is important to be clear about which models can be used and whether the data used is reliable.

How to use the Library in Python?

The Iris Dataset is a popular training dataset for creating a classification algorithm. It is an example from biology and deals with the classification of so-called iris plants. About each flower the length and width of the petal and the so-called sepal are available. Based on these four pieces of information, it is then to be learned which of the three iris types this flower is.

With the help of Skicit-Learn, a decision tree can be trained in just a few lines of code:

# Import Modules
from sklearn.datasets import load_iris
from sklearn import tree

# Load Iris Dataset
iris = load_iris()

# Define X and Y Variable
X, y = iris.data, iris.target

# Set up the Decision Tree Classifier
clf = tree.DecisionTreeClassifier()

# Train it on the Iris Data
clf = clf.fit(X, y)

So we can train a decision tree relatively easily by defining the input variable X and the classes Y to be predicted, and training the decision tree from Skicit-Learn on them. With the function “predict_proba” and concrete values, a classification can then be made:

# Predict class for artificial values
clf.predict_proba([[4.5, 8.2, 2.1, 1.7]])

Out: 
array([[1., 0., 0.]])

So this flower with the made-up values would belong to the first class according to our Decision Tree. This genus is called “Iris Setosa”.

This is what you should take with you

  • Scikit-Learn (also known as sklearn for short) is a Python library with which machine learning applications can be implemented in just a few lines of code.
  • The library can be used for various applications in the areas of classification, dimensionality reduction, or regression.
  • Sklearn is very popular because it is based on Numpy, is easy to use, and offers a high degree of flexibility.

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